2a:T503,

Most existing warranty policies are rigid, and the downtime loss is also not taken into account. This study develops a two-stage decision framework to design flexible warranty policies, where the downtime loss is considered. In the first stage, by minimizing the warranty service cost, a fixed warranty policy is provided to determine the baseline of preventive maintenance’s times and effort. In the second stage, customers have three options to increase preventive maintenance times, preventive maintenance effort, or both of them, which results in three types of flexible warranty policies. The additional maintenance cost for the increased preventive maintenance times and/or preventive maintenance effort is paid by the customers. Besides, the flexible policies are optimized to minimize customer’s cost, which is the sum of the downtime loss and shared maintenance cost. A practical example is provided to illustrate the effectiveness of the proposed flexible warranty policies. The results indicate that compared with the fixed warranty policies, both the manufacturer and customers can benefit from the proposed flexible policies, especially when the downtime loss is substantial. Moreover, the proposed policy is more effective when the warranty period is longer.

2b:T530,

An important challenge in structural reliability is to reduce the number of calls to evaluate the performance function, especially the complex implicit performance functions. To reduce the computational burden and improve the reliability analysis efficiency, a new active learning method is developed to consider the probability density function of samples based on the learning function U in an active learning reliability method that combines the kriging and Monte Carlo simulation. In the proposed method, the proposed active learning function contains two parts: part A is based on function U, and part B is based on the probability density function and function U. By changing the weights of parts A and B, the sample points close the limit-state function, and those in the region with a higher probability density function have more weight to be selected compared to the others. Subsequently, the kriging model can be constructed more effectively. The proposed method avoids a large number of time-consuming function evaluations, and the recommended weight is also reported. The performance of the proposed method is evaluated through three numerical examples and one engineering example. The results demonstrate the efficiency and accuracy of the proposed method.

2c:T402,The Journal of Risk and Reliability is for researchers and practitioners who are involved in the field of risk analysis and reliability engineering. The remit of the Journal covers concepts, theories, principles, approaches, methods and models for the proper understanding, assessment, characterisation and management of the risk and reliability of engineering systems. The journal welcomes papers which are based on mathematical and probabilistic analysis, simulation and/or optimisation, as well as works highlighting conceptual and managerial issues. Papers that provide perspectives on current practices and methods, and how to improve these, are also welcome. This journal is fully peer reviewed and is a member of the <a href="http://publicationethics.org/about">Committee on Publication Ethics (COPE)</a>. Submit your manuscript today at <a href="https://mc.manuscriptcentral.com/JRR">https://mc.manuscriptcentral.com/JRR</a>.